根据2列在另一个表中添加缺失的行

Question

I have a subset of a dataframe like bellow

ID  var1 var2 var3
111  A    1    1
222  A    1    1
333  A    1    1
444  A    2    1
555  A    2    1
666  A    2    1

and I want to join missing information from dataframe bellow. But only those ID that subset contains var1 and var2

ID  var1 var2 var3
111  A    1    1
222  A    1    1
333  A    1    1
777  A    1    0
888  A    1    0
444  A    2    1
555  A    2    1
666  A    2    1
999  A    2    0
123  B    3    1
456  B    4    0
789  C    5    1

So output should be

ID  var1 var2 var3
111  A    1    1
222  A    1    1
333  A    1    1
777  A    1    0
888  A    1    0
444  A    2    1
555  A    2    1
666  A    2    1
999  A    2    0

Thanks!

Answer 1

Use merge

In [164]: df2.merge(df1[['var1', 'var2']].drop_duplicates())
Out[164]:
    ID var1  var2  var3
0  111    A     1     1
1  222    A     1     1
2  333    A     1     1
3  777    A     1     0
4  888    A     1     0
5  444    A     2     1
6  555    A     2     1
7  666    A     2     1
8  999    A     2     0

Answer 2

Although Zero already answered. You could also use Pandas Library and it's DataFrame module. It's very easy to use and understand; using just indexes you can sort, iterate, aggregate, concactenate, visualize, and sparse data.

Combining it with NumPy 's ndarray makes it even easier to manipulate. TutorialsPoint has a great tutorial on how to combine the two (basic functionality) Python Pandas - Basic Functionality .

Example

'''
ID  var1 var2 var3
111  A    1    1
222  A    1    1
333  A    1    1
444  A    2    1
555  A    2    1
666  A    2    1
'''
import pandas as pd

data = [
        [111, 'A', 1, 1],
        [222, 'A', 1, 1],
        [333, 'A', 1, 1],
        [444, 'A', 2, 1],
        [555, 'A', 2, 1],
        [666, 'A', 2, 1]
       ]

df = pd.DataFrame( data, columns = [ 'ID', 'var1', 'var2', 'var3' ] )

print(df)

Output

    ID var1  var2  var3
0  111    A     1     1
1  222    A     1     1
2  333    A     1     1
3  444    A     2     1
4  555    A     2     1
5  666    A     2     1